1. Field of the Invention
The present invention is directed generally to the field of business accounting systems and, more particularly, to methods and apparatus for optimizing production and distribution supply chains as well as other complex systems.
2. Description of the Background
Production and distribution supply chains have grown into large and complex systems. Consider, for example, an automobile manufacturer. The thousands of components that go into an automobile are supplied by hundreds, perhaps thousands, of individual suppliers, each having its own suppliers, production facilities, labor costs, transportation costs, etc. The supply chain for such large enterprises is a very complex system and, unfortunately, is a result more of history than of a deliberate effort to build an optimized system. After the supply chain is in place, decisions tend to be localized such that their impact on the entire supply chain is not known until some history of operation under that decision is gathered. Even then, supply chains tend to be so complex and poorly understood, that even with historical operational data, it is often not known how best to change operations to move the entire system to a more optimized condition.
Current business techniques, such as activity based costing, provide a more reliable way of viewing a company's operations. In activity based costing, the cost and performance of activities and products is measured and quantified. An example of an automated, activity-based management system is disclosed in U.S. Pat. No. 5,799,286. Disclosed in that patent is a system in which traditional ledger accounting information and human resources information is used with other information directed to activities, equipment usage, and facilities utilization to generate costs associated with activities performed by the organization. The traditional accounting information and the activity information are fed into a relational database. The information is processed and costs associated with the employee, facilities, equipment, and overhead components of activities are computed. User defined reports for trending, forecasting, comparison, benchmarking, and budgeting purposes can be generated.
While such systems are a step forward, they represent the type of localized decision making that is not well suited to complex systems such as production and distribution supply chains. As mentioned previously, optimizing one organization within a production and distribution supply chain may lead to problems elsewhere in the system. Furthermore, traditional activity based costing models do not take into account non-economic considerations. Supply chains can have various non-economic measures of performance, which may or may not be important in optimizing the system. Some examples of these would include:                a. Product freshness: This measure is particularly important in supply chains in the food industry (for example, fast food restaurant chain), where the considerations of freshness will be as important as (if not more than) a purely economic decision. That is, some less than optimum procedures may need to be tolerated (deliveries every day at higher cost as opposed to every four days at lower cost) to ensure freshness of goods.        b. Product Lifecycle: This refers to the total time spent in a supply chain from the inception and design of a product to its ultimate delivery to an end customer. This is an important issue in industries with short lifecycles such as the garment industry, where the demand can be very unpredictable.        c. Robustness of solutions. The final optimized solution for the supply chain is with one set of values for the underlying variables. The range of the values for these variables, for which the solution is still a valid optimal solution, is the measure of the robustness of the solution.        
Traditional activity based costing methods also do not provide sufficient flexibility to account for uncertainties in the market place. The most common and important source of uncertainty for a supply chain is likely to be in the projected demand for products. However, depending on the nature of the specific supply chain, the design may be very sensitive to factors such as raw material costs and availability (especially with commodity market sourcing) and exchange rates (in more international supply chain). In general, the uncertainty can occur due to:                a. Demand uncertainty—Product volumes, product mix        b. Supply uncertainty—Raw materials availability, raw material costs        c. Resource availability—Labor, unscheduled downtime of resources (lines, plants), costs of resources (labor, fuel)        d. Other factors—e.g. interest and exchange ratesIt is important to note that each of these can have seasonal, promotional and regional/national effects. A key issue then is to design a system that is flexible in handling a large range of each of these variables, and to know the costs of building in this flexibility into the system.        
To date, there is no system for modeling a complex supply chain, in an integrated manner, and that takes into account non-economic factors and market uncertainties. Accordingly, the need for such a system exists.